Projects per year
Abstract
We propose a novel method to locate spruces in a young stand with a low cost unmanned aerial vehicle. The method has three stages: 1) the forest area is mapped and a digital surface model and terrain models are generated, 2) the locations of trees are found from a canopy height model using local maximum and watershed algorithms, and 3) these locations are used in a convolution neural network architecture to detect young spruces. Our result for detecting young spruce trees among other vegetation using only color images from a single RGB camera were promising. The proposed method is able to achieve a detection accuracy of more than 91%. As low cost unmanned aerial vehicles with color cameras are versatile today, the proposed work is enabling low cost forest inventory for automating forest management.
Original language | English |
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Pages (from-to) | 15777–15783 |
Number of pages | 7 |
Journal | IFAC-PapersOnLine |
Volume | 53 |
Issue number | 2 |
DOIs | |
Publication status | Published - Nov 2020 |
MoE publication type | A4 Conference publication |
Event | IFAC World Congress - Virtual, Online Duration: 11 Jul 2020 → 17 Jul 2020 Conference number: 21 |
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Dive into the research topics of 'Drone based Mapping and Identification of Young Spruce Stand for Semiautonomous Cleaning'. Together they form a unique fingerprint.Projects
- 2 Finished
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Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing – Point Cloud Ecosystem
Visala, A. (Principal investigator)
01/01/2018 → 31/07/2021
Project: Academy of Finland: Strategic research funding
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COMBAT: Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing - Point Cloud Ecosystem
Hyyppä, H. (Principal investigator)
01/05/2015 → 31/12/2017
Project: Academy of Finland: Strategic research funding